This
manuscript proposes using empirical Bayes techniques on estimated density
values from nonparametric kernels in attempts to exploit potential similarities
among a set of unknown densities. Our asymptotic theory and simulation
results suggest that the emipirical Bayes nonparamtetric kernel estimator
may be a viable alternative to the standard kernel estimator when a set
of possibly similar densities are being estimated. The strengths of the
proposed estimator are (i) it allows all types of kernel estimators; and
(ii) it does not require specification as to the degree or form of similarity.